Automated Identification of Heavy BIM Library Components: A Multi-Criteria Analysis Tool for Model Optimization

The growing complexity of BIM (Building Information Model) models leads to perfor-mance issues, extended file loading times, and difficulties in cross-industry coordina-tion. One of the main factors reducing performance are so-called "heavy" library com-ponents (families in Revit), characterized by excessive geometric complexity, a large number of instances, or improper optimization. Currently, the identification of such components is based mainly on the experience of designers and manual inspection of models, which is time-consuming and prone to errors. This article presents a new tool, HeavyFamilies, which automates the detection and analysis of heavy library compo-nents in BIM models. The tool uses a multi-criteria analysis method, evaluating com-ponents based on five key parameters: number of instances, geometry complexity, number of walls and edges, and estimated file size. Each parameter is weighed ac-cording to its impact on model performance. The developed solution has been imple-mented as a pyRevit plugin for Autodesk Revit, offering a graphical interface with a tabular summary of results, a CSV export function, and visualization of detected components directly in the model. Validation of the tool on real BIM projects has demonstrated its effectiveness in identifying components with a weight index exceed-ing the threshold of 200, allowing designers to prioritize optimization efforts. The HeavyFamilies tool is a practical contribution to the field of BIM model optimization, enabling a systematic approach to managing model performance in complex construc-tion projects and supporting the development of smart cities.